Apache Mahout Essentials

Apache Mahout Essentials is published by Packt Publishing in June 2015. This book has 151 pages in English, ISBN-13 978-1783554997.

Apache Mahout is a scalable machine learning library with algorithms for clustering, classification, and recommendations. It empowers users to analyze patterns in large, diverse, and complex datasets faster and more scalably.

This book is an all-inclusive guide to analyzing large and complex datasets using Apache Mahout. It explains complicated but very effective machine learning algorithms simply, in relation to real-world practical examples.

Starting from the fundamental concepts of machine learning and Apache Mahout, this book guides you through Apache Mahout’s implementations of machine learning techniques including classification, clustering, and recommendations. During this exciting walkthrough, real-world applications, a diverse range of popular algorithms and their implementations, code examples, evaluation strategies, and best practices are given for each technique. Finally, you will learn vdata visualization techniques for Apache Mahout to bring your data to life.

What You Will Learn

Get started with the fundamentals of Big Data, batch, and real-time data processing with an introduction to Mahout and its applications

Discover tips and tricks to improve the accuracy and performance of your results

Set up Apache Mahout in a production environment with Apache Hadoop

Glance at the Spark DSL advancements in Apache Mahout 1.0

Provide dynamic and interactive data visualizations for Apache Mahout

Build a recommendation engine for real-time use cases and use user-based and item-based recommendation algorithms

Who This Book Is For

If you are a Java developer or data scientist, haven’t worked with Apache Mahout before, and want to get up to speed on implementing machine learning on big data, then this is the perfect guide for you.